Analisis Sentimen Masyarakat terhadap Program Makan Siang Gratis di Indonesia Tahun 2024 Menggunakan Long Short-Term Memory (LSTM)
DOI:
https://doi.org/10.61132/merkurius.v3i4.930Keywords:
Sentiment Analysis, Free Lunch Program, Long Short-Term Memory (LSTM)Abstract
The free lunch program is a goverment initiative aimed at addressing the issue of stunting in Indonesia. This program focuses on toddlers, school-age children and pregnant women. Various opinions have emerged from the public regarding this initiative, especially through sosial media platform X (Twitter) and news portals. In this research, sentiment analysis was conducted to understand public responses to the program, whether they are positive, neutral or negative. To evaluate the accuracy of the sentiment analysis perfomed, a deep learning approach was applied using the Long Short-Term Memory (LSTM) algorithm. The results show that public sentiment varies responses, on social media X tend to be negative, while those on news portals tend to be positive toward the free lunch program in Indonesia. Through LSTM-based testing, sentiment analysis on tweet data achieved an accuracy of 88.6%, with a precision of 84.6%, recall of 88.6% and an F1-Score of 86.3%. Meanwhile, sentiment analysis on news portal data reached an accuracy of 89%, with a precision of 81.7%, recall of 89% and an F1-Score of 85.1%.
References
Aakash, Gupta, S., & Noliya, A. (2024). URL-based sentiment analysis of product reviews using LSTM and GRU. Procedia Computer Science, 235(2023), 1814–1823. https://doi.org/10.1016/j.procs.2024.04.172
Al-Areef, M. H., & Saputra S, K. (2023). Analisis sentimen pengguna Twitter mengenai calon presiden Indonesia tahun 2024 menggunakan algoritma LSTM. Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer), 22(2), 270. https://doi.org/10.53513/jis.v22i2.8680
Ardelia Maharani, P., Riyani Namira, A., & Viony Chairunnisa, T. (2024). Peran makan siang gratis dalam janji kampanye Prabowo Gibran dan realisasinya. Jolasos: Journal of Law and Social Society, 1–10.
Ardian Pradana, Y., Cholissodin, I., & Kurnianingtyas, D. (2023). Analisis sentimen pemindahan ibu kota Indonesia pada media sosial Twitter menggunakan metode LSTM dan Word2Vec. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 7(5), 2389–2397. http://j-ptiik.ub.ac.id
Elkabalawy, M., Al-Sakkaf, A., Mohammed Abdelkader, E., & Alfalah, G. (2024). CRISP-DM-based data-driven approach for building energy prediction utilizing indoor and environmental factors. Sustainability, 16(17), 7249. https://doi.org/10.3390/su16177249
Ganesha, U. P., Korespondensi, P., Sentimen, A., & Allocation, L. D. (2024). English sentiment analysis using the LSTM method case study of. Jurnal Teknologi Informasi dan Ilmu Komputer, 11(6). https://doi.org/10.25126/jtiik.2024118792
Gouthami, S., & Hegde, N. P. (2023). Sentiment analysis based Twitter tweets classification using data embedded with LSTM technique. Journal of Theoretical and Applied Information Technology, 101(4), 1264–1272.
Hafiz, Y. A., & Sudarmilah, E. (2023). Implementasi web scraping pada portal berita online. Inisiasi, 55–60. https://doi.org/10.59344/inisiasi.v12i1.120
Hidayati, A. R., Fitrani, A. S., Rosid, M. A., Sains, F., & Teknologi, D. (2023). Analisa sentimen Pemilu 2019 pada judul berita online menggunakan metode logistic regression. Kesatria: Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 4(2), 298–305. http://www.pkm.tunasbangsa.ac.id/index.php/kesatria/article/view/164
Isnain, A. R., Sulistiani, H., Hurohman, B. M., Nurkholis, A., & Styawati, S. (2022). Analisis perbandingan algoritma LSTM dan Naive Bayes untuk analisis sentimen. Jurnal Edukasi dan Penelitian Informatika (JEPIN), 8(2), 299. https://doi.org/10.26418/jp.v8i2.54704
Kande, J. (2024). Twitter sentiment analysis with LSTM neural networks. REST Journal on Data Analytics and Artificial Intelligence, 3(3), 92–98. https://doi.org/10.46632/jdaai/3/3/11
Mahadevaswamy, U. B., & Swathi, P. (2022). Sentiment analysis using bidirectional LSTM network. Procedia Computer Science, 218, 45–56. https://doi.org/10.1016/j.procs.2022.12.400
Maulana, A. R., Wijoyo, S. H., & Mursityo, Y. T. (2023). Sentiment analysis of implementation independent curriculum policy elementary and secondary school on Twitter using word embedding and LSTM. Jurnal Teknologi Informasi dan Ilmu Komputer, 10(3), 523–530. https://doi.org/10.25126/jtiik.2023106977
Nursinggah, L., Ruuhwan, R., & Mufizar, T. (2024). Analisis sentimen pengguna aplikasi X terhadap program makan siang gratis dengan metode Naïve Bayes classifier. Jurnal Informatika dan Teknik Elektro Terapan, 12(3). https://doi.org/10.23960/jitet.v12i3.4336
Rismawan, S. A., & Syahidin, Y. (2023). Implementasi website berita online menggunakan metode crawling data dengan bahasa pemrograman Python. Jurnal Teknik Informatika dan Sistem Informasi, 10(3), 167–178. http://jurnal.mdp.ac.id
Shiri, F. M., Perumal, T., Mustapha, N., & Mohamed, R. (2023). A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU. Machine Learning, 1–61. http://arxiv.org/abs/2305.17473
Sitanggang, A., Umaidah, Y., Umaidah, Y., Adam, R. I., & Adam, R. I. (2024). Analisis sentimen masyarakat terhadap program makan siang gratis pada media sosial X menggunakan algoritma Naïve Bayes. Jurnal Informatika dan Teknik Elektro Terapan, 12(3). https://doi.org/10.23960/jitet.v12i3.4902
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.